code: def _distorted_bounding_box_crop(
      image_bytes: tf.Tensor,
      jpeg_shape: tf.Tensor,
      bbox: tf.Tensor,
      min_object_covered: float,
      aspect_ratio_range: Tuple[float, float],
      area_range: Tuple[float, float],
      max_attempts: int,
  ) -> tf.Tensor:
      """Generates cropped_image using one of the bboxes randomly distorted."""
      bbox_begin, bbox_size, _ = tf.image.sample_distorted_bounding_box(
          jpeg_shape,
          bounding_boxes=bbox,
          min_object_covered=min_object_covered,
          aspect_ratio_range=aspect_ratio_range,
          area_range=area_range,
          max_attempts=max_attempts,
          use_image_if_no_bounding_boxes=True,
      )
  
      # Crop the image to the specified bounding box.
      offset_y, offset_x, _ = tf.unstack(bbox_begin)
      target_height, target_width, _ = tf.unstack(bbox_size)
      crop_window = tf.stack(
          [offset_y, offset_x, target_height, target_width]
      )
      image = tf.image.decode_and_crop_jpeg(
          image_bytes, crop_window, channels=3
      )
      return image
  
file_path: objax/examples/image_classification/imagenet_resnet50_data.py
imports: import enum
  from typing import Optional, Sequence, Tuple
  import jax
  import numpy
  import tensorflow.compat.v2
  import tensorflow_datasets
label_desc: Use image node_id 7 for calling a global function with example usage: _distorted_bounding_box_crop(image_bytes, jpeg_shape, bbox, min_object_covered, aspect_ratio_range, area_range, max_attempts) and returns: image